Our interface does not allow the user to view referring expressions once it has been entered.

if the same object is marked again in the series, the user will have no way to look up what they had written before.


We cannot expect our models trained on the Diagnostic data to handle multiple past referring expressions.

when an object is associated with multiple past expressions, we only consider the expression most similar to the query expression according to the Unsupervised InferSent model.


Note that in the 0-column, the 0 only refers to the ground-truth object not having a referring expression -the other objects may have expressions associated with them.

the supervised models can better determine when two expressions are incompatible, leading to the model choosing an object without any previous referring expression.


Put differently, the oracle policy selects the set of unlabeled examples that maximizes the target metric of our model on a set sampled from the same distribution as the test set.

the oracle policy enjoys extremely favorable conditions compared to a trained policy, and we expect it to provide an upper bound on the performance of π θ .


Moreover, the parameter L controls the diversity of candidate subsets, since as L increases the similarity between the K different subsets grows.

the hyper-parameters K and L might affect the outcome of the oracle policy.


We start with a labeled set of size S lab = 500 and perform B = 250 iterations, where in each iteration we sample K = 5 candidate sets each of size L = 10 (lower values were intractable).

the final size of S lab is 3,000 samples.


The only difference between the two fine-tuning procedures is the random seed.

an MRR value that is close to 1 means that the ranking of the candidates is mostly affected by the quality of the samples, while a small MRR hints that optimization plays a large role.


The respective English pairs are split in the same way, according to the gender of the nouns in the GM language.

we end up with two sets of pairs in a GM language and their translations to English.


Once a label is selected, it is transmitted to the corresponding model and triggers the selection/generation of the next instance.

at any given time there is one unlabeled instance for each model or baseline.


Nevertheless, for each claim, the corpus provides 30 documents which are retrieved from the web using the Google search engine instead of a document collection aggregated by fact-checkers.

many of the documents are unrelated to the claim and important information for the validation may be missing.


Many of the ETSs have been annotated as no stance (see Table 5) and, following our annotation study setup, are not used for FGE extraction.

the number of FGE sets is much lower than that of ETSs.


We formulate the claim validation problem in such a way that we can compare it to the FEVER recognizing textual entailment task.

as illustrated in Table 8, we compress the different verdicts present on the Snopes webpage into three categories of the FEVER shared task.


However, compared to the FEVER claim validation problem,  where systems reach up to 0.7 F1 macro, the scores are relatively low.

there is ample opportunity for improvement by future systems.


However, no significant performance gain could be observed in both experiments Based on our analysis, we conclude that heterogeneous data and FGE from unreliable sources, as found in our corpus and in the real world, make it difficult to correctly classify the claims.

in future experiments, not just FGE need to be taken into account, but also additional information from our newly constructed corpus, that is, the stance of the FGE, FGE sources, and documents from the Snopes website which provide additional information about the claim.


Figure 2b shows an example: here, a location (Texas) exists but is labeled as a non-entity, because the original dataset from which this sentence is drawn does not annotate locations at all.

this approach suffers from false penalties when applying the original likelihood function (Eq.


However, lives, in, and Texas are labeled as O (nonentity), which here means only that they may not be PER-but any of them could be LOC, since locations are not annotated for this sentence.

any sequence that assigns either O or LOC for any of these three positions is consistent with the gold labels.


However, many O labels are confident annotations of O.

naively training with the improved gold energy function tends to over-predict entities and not predict Os.


Nevertheless, to our knowledge, our work is the first combination of these ideas for entity linking.

we demonstrate the first accurate, robust, and highly efficient system that is actually a viable substitute for standard, more cumbersome twostage retrieval and re-ranking systems.


For each round, we mine negatives from the current model as described above and then append the new hard examples to the classification task.

each subsequent round of negative mining adds fewer and fewer new examples, which yields a stable and naturally convergent process.


Cognitive lexical semantics proposes that words are defined by how they are organized in the brain (Miller and Fellbaum, 1992).

brain activity data recorded from humans processing language is arguably the most accurate mental lexical representation available (Søgaard, 2016).


For continuous stimuli, this means that the response to previous stimuli will have an influence on the current signal.

context of the previous words is taken into account the preprocessed signal averaged for six regions of interest, hence for this particular dataset we predict the activation for these regions only.


However, this event sequence appears infrequently in text.

2 only relying on event cooccurrence in text is not sufficient -there is also a need to model some "common sense" information.


For ease of explanation, we denote a semantic sequence of joint event representations as [e 1 , e 2 , • • • , e t ], with e t being the t th event in the sequence.

we model the conditional probability of an event e t given its context as Note that the softmax operation is carried out over the event vocabulary V, i.e., all possible events in the language model.


Here, we use the bi-focal attention mechanism (Nema et al., 2018) via attention parameters W a , W b , and apply it on the hidden layer h t , which embeds information from all previous events in the sequence.

we produce a distribution over the set of possible outcome events V y .


The sequence generation model then combines the local and global context for generating future events.

we model the conditional probability of event e t+1 given context p(e t+1 |Context) = p(e t+1 |e 1 , e 2 , • • • , e t , KB EC ).


In practice, we get at most 12 events as context since one sentence can contain multiple events.

for each story, we generate at most 12 pairs of conditional probability features from two given choices.


We validate the effectiveness of our proposed model by addressing two important natural language tasks: a text classification task using two standard datasets (i.e., the 20 Newsgroups and R8 datasets), and a factoid question answering task based on a popular dataset derived from the quiz bowl trivia quiz game.

our model achieved state-of-the-art results on both tasks.


We used 20% of questions each for the development set and test sets, and the remaining 60% for the training set.

the training, development, and test sets consisted of 1,535, 511, and 511 questions for the history category, and 2,524, 840, and 840 questions for the literature category.


Invariably, all works resort to the lexical cohesion theory (Halliday and Hasan, 1976), which postulates that discourse structure is correlated to the use of cohesive vocabulary.

segments can be identified by detecting vocabulary changes.


These multi-document models assume that documents describing the same topic have similar lexical cohesion properties; an example of this phenomenon with similar segments but in different documents is depicted in Figure 1.

better likelihood estimations can be obtained if all documents are taken into account (Mota et al., 2016).


We implement lexical cohesion in a Bayesian setting in a generative process where segments with the same topic are drawn from the same multinomial language model.

all u utterances with a topic k have their bag-of-words representation x u drawn from language model φ zu ; z u is the hidden topic variable of u.


This dataset does not provide topic identification labels for the segments.

we manually annotated it with this information.


It is worthy to note that, α i t is used as the probability that position index i is selected as output at time step t. At time step t, the position index with the highest probability in α t is greedily sampled as the output O t .

once the attention weights are computed, the context vector c t is computed by: Then c t together with d t−1 are fed into LSTM decoder at time step t: the LSTM decoder is capable of tracking the state of the variable-length entity pair predictor.


The nearby words of an entity can give significant information about the entity.

we use the tokens of an entity and its nearby tokens to obtain its vector representation.


Due to the normalization of the attention scores as described above, single attention cannot capture all relevant information needed to find the relation between two entities.

we use a multifactor attention mechanism, where the number of factors is a hyper-parameter, to gather all relevant information for identifying the relation.


Since there is an enormous and relentless linguistic and philosophical debate on this topic (Rey, 2018), we decided not to delimit these categories and not to distinguish them.

they have been labeled together as 'sentence level context' (SL-C).


Therefore, they have been labeled together as 'sentence level context' (SL-C).

we ended up with a set of categories for annotating the items (sentences) from our data set, which are presented in Table 2 as well as described briefly in the following sections and more precisely in the supplementary materials.


3), SL-C (sentence level context) and DL-CR (document level co-reference) co-occur the most often.

if a model also takes into account the context of a whole document, it can be of great benefit.


However, exact knowledge of entity ratio is unlikely in the absence of gold data.

we experimented with selecting a default b value, and using it across all languages, with the Cogcomp model.


Since our work focuses on event relations, we build our models to predict relations between EE pairs only when conducting experiments.

all micro-average F1 scores only consider EE pairs.


Given a contextualized token, the classifier computes the probability of an entity link for each entry in the entity vocabulary.

formally, let d be BERT's token embedding size, and E ∈ R |KB|×d the entity classification layer, with |KB| being the number of entities in the KB, V is the sub-word vocabulary, the probability p(j|v, h) of word v -which is the i-th token in context h -linking to entity j is computed by σ(E j c i ), where σ is the sigmoid function.


the label y vector for one token v i is defined by However, computing the loss over the whole entity vocabulary would be infeasible, because the entity mention vocabulary is very large and the gradients for the entity classifier would exceed our GPU memory.

to improve memory efficiency and increase convergence speed, we use negative sampling.


Since the problems in RACE are designed for human participants that require advanced reading comprehension skills such as the utilization of external world knowledge and in-depth reasoning, even human annotators sometimes have difficulties in locating evidence sentences (Section 3.6).

a limited number of evidence sentences might be insufficient for answering challenging questions.


", neither "find out" nor the correct answer option appears in the given document as this question mainly assesses the vocabulary acquisition of human readers.

all the extracted sentences (e.g., "sometimes... sometimes I feel lonely, like I'm by myself with no one here.


A key requirement in these systems is the ability to efficiently parse user queries, understand the intent behind each query, and provide adequate responses to users.

many applications such as conversation bots and smart IoT devices has a natural language understanding (LU) service integrated within.


As an example: the utterance "I want a large pizza" can be of type Order Food for a restaurant application, while the same utterance can be of type None (i.e., outlier) for a bus-tracking application.

application-determined vector representations are essential for capturing the semantics of utterances in LU services and hence accurate mapping to user-defined intents.


The different sub-metrics have different relative importances in detecting similarities between utterances within the application.

the weights should be tuned automatically according to the LU application.


they contain a user-defined label for every utterance, which serves as our ground truth.


At this point, we use each row as the vector representation of the corresponding utterance.

each representative utterance serves as a dimension in the vector space, allowing utterances with similar neighbors to have similar vector representations.


This is achieved by taking the max value of each entry of the corresponding indexes.

the resulting vector is closer to neighbors of both points, shrinking the distances between neighbors of both utterances.Moreover, because each feature (dimension) in the space is a representative utterance, after the collapsing of two utterances, we perform PCA to come up with the new dimensions after the user is done with the labeling.


For instance, in Figure 1, a 3 plays different roles in different dialog turns: speaker in the turn 2 and n-1, addressee in the turn n and n+1.

it is very important for RGMPC to capture interlocutor information.


Furthermore, from the view of conversation analysis, responses are usually used for answering the addressee's question or expanding the addressee's utterances.

we originally introduce an addressee memory mechanism to enhance contextual information for the target addressee especially.


Moreover, the addressee memory layer records contextual word representation for the target addressee.

we extract contextual speaker vector A res for the responding speaker a res from the final interlocutor embedding matrix A (e.g., the responding speaker's embedding obtained by A m = A[ * , a m ] for the responding speaker a m in Figure 2).


The average dialogue turn for the interlocutor is more than 5000 in (Li et al., 2016a), while there is less than 100 dialogue turns per interlocutor in our dataset.

it is hard to learn a global vector for each interlocutor from the sparse corpus.


In contrast, ICRED exploits interactive interlocutor representation learned from current dialog context rather than the fixed person vectors obtained from all training dialog utterances.

iCRED has no sparsity issues and it performs closely on sparse and plentiful learning data.


Phrases and ideas from various parts of the context need to come together in order to fully understand whether or not a question is answerable.

our relation module takes all pairs of context objects to score, and use the question objects to guide the scoring function.


Firstly, since the problem has not been studied before, there are no existing datasets to use.

we built a multiturn dataset for two H2H domains that we found to be prevalent in Twitter conversations: Music and Restaurants.


The task is quite different for SDP argument labeling, however, as a word's label highly depends on the word's context and the corresponding relation the word is associated with.

we cannot apply such an approach directly and hence define an aggregation procedure for combining the individual per-window predictions.


In contrast, our aim is to explore to what extent the problem can be solved without feature engineering.

the main inspiration for our approach is the recent work of Hooda and Kosseim (2017), who use an LSTM network on spans of text for labeling arguments.


Then, for each window, we predict whether it contains a (possibly partial) relation or not, and which tokens belong to a particular part of the relation.

for each token our model predicts one of four classes, None, Arg1, Arg2, or Conn. We illustrate the process using the first example from Table 1.


Therefore, we choose the centering of the beginning of Arg2 as a sufficient criterion (for generality to potentially cover both explicit and implicit relations, we did not choose the connective position as identifier).

we extract three windows around the words " The main challenge in jointly predicting connectives and their arguments is caused by multiple relations with overlapping arguments.


The input to the Influence Generator is the r×n dimensional vector of topic distributions from previous r timestamps of a related text stream, i.e., θ T −r:T −1 (assuming T as the current timestamp).

influence Generator essentially maps a r × n dimensional topic vector to a K dimensional influence vector.


The process can be thought of as generating text corresponding to a particular timestamp T .

the whole process starts with a timestamp T as input and the start-of-sentence marker (let's call it #) as the sequence generated so far.


This creates exposure bias (Ranzato et al., 2015) during training, since the model is only exposed to the data distribution and not the model distribution.

performance suffers due to inadequately evaluating the structure of the generated question against the ground-truth question.


We use this attention a t i to generate the context vector c * t as a weighted sum of encoder hidden states: We further use the c * t vector to obtain a probability distribution over the words in the vocabulary as: , where W v and b v are model parameters.

during decoding, the probability of a word is P (qword).


As discussed earlier, Equation 5addresses the rare words issue, since a word not in vocabulary will have probability p(qword) = 0.

in such cases, our model will replace the <unk> token for out-of-vocabulary words with a word in the input sentence having the highest attention obtained using attention distribution a t i .


As discussed in section 2.2.1 DAS will give high reward even though the generated question has low BLEU score.

the performance of the model on automatic evaluation metrics does not improve with DAS as the reward function, though the quality of questions certainly improves.


When selecting the activation function, since we expect the final predicted value of the scaling factors to cover the range of 0∼1, we applied the Sigmoid activation function to the original outputs of the scaling gate.

the scaling gate takes the x ∈ R h and the F(x, W) ∈ R h as input and computes the output through two linear transformations with a Tanh activation in between: are the parameters to be learned, and S(x, F) is followed by a Sigmoid activation function.


Especially, it is essential to decrease the parameters to achieve the same purpose 1 .

the final selfadaptive scaling approach can be defined by the following formula: where α and β act as the scaling factors predicted as aforementioned.


The batch normalization is used commonly in the field of computer vision.

we replace the layer normalization with the batch normalization in the proposed approach to see the difference.


Most ML approaches to Natural Language Processing (NLP) address modern, high-resource languages (such as English or Chinese) rather than historical, low-resource languages.

feasible ML-tools for processing historical documents are still rare.


"F. sylvatica" after the first definition).

standard rulebased tools often fail to detect the correct sentence boundaries in such unstructured raw text documents.


However, the aspect of time is important for the research on biodiversity which is constantly evolving.

we annotated every text unit   Table 9).


Data Merging for BiLSTM Tagger For BiLSTM-a, it can be noted that the performance of the standard categories PERSON, ORGANIZATION, and, especially LOCATION is inferior.

we performed resourceoptimization by merging high quality data with our BIOfid dataset in order to increase the training samples for the low performing categories.


This effect is especially well visible for the Alleviated TOI 10.  dataset to a total of P • M data points, adding additional data points with token offsets.

the P •M K × K matrix used for batch creation may contain repeated data points within the same batch as illustrated in Figure 5.


The similar instances are denoted by a label y = 1 whereas the dissimilar pairs are represented by y = 0.

the loss function has the following form: Each term is expressed by: This loss function measures how well the model learns to encode similar transformations such that analogous pairs are mutually close and form an analogical parallelogram in the embedding space, while pushing dissimilar transformations apart.


When we discard the infrequent words, we only narrow down the input vocabulary and do not change the output vocabulary.

the perplexity scores are still comparable with the scores in the above experiments.


We hypothesise that in general, the norm difference indicates the divergence between optimizers' steps and we observe that the larger the norm difference, the greater the change in the trajectory.

we propose to Algorithm 2: Norm-filtered Aggressive SWA algorithm Require: 1: e = Epoch number 2: m = Total epochs 3: i = Iteration number 4: n = Total iterations 5: α = Learning rate 6: O = Stochastic Gradient optimizer function 7: N s = List of previous iterations' norm differences e ← 0; while e < m do e ← e + 1 maintain a list that stores the norm differences of the previous iterations.


Work on Abusive Language Detection has tackled a wide range of subtasks and domains.

of this, there exists a great deal of redundancy and non-generalisability between datasets.


Note that it is a common procedure to fix training and test documents in order to ensure reproducibility .

we created a balanced, singletopic dataset from each site, containing different numbers of authors depending on the size of the community of the respective site.


This makes especially sense as we are dealing with 179 different datasets of different sizes, where a comparison of the top-k results with a fixed k is not meaningful (e.g., it makes a huge difference if the correct author is in the top-5 in a dataset containing 20 authors or in one containing 16,000 authors).

we experimented with the reduction rates 10-90%, 95%, and 99%, and measured the hit rate, i.e., the percentage of how often the correct author is still in the reduced candidate set.


It will be of great significance if numerous unlabeled samples can be utilized to further facilitate the supervised ATSA classifier.

the semi-supervised ATSA is a promising research topic.


In contrast to the conventional auto-regressive models, the latent representations have their specific meanings and are obtained from the encoder and the classifier to the input examples.

it is also possible to condition the sentence generation on the sentiment and lexical information w.r.t.


The true opinion snippet "certainly is not a relaxing place" receives low attention weights, leading to the wrong prediction towards the aspect place.

we propose an alternative hardselection approach by determining two positions in the sentence and selecting words between these two positions as the opinion expression of a given aspect.


We observe that utilizing the proposed approach leads to accurate health risk appraisal well in advance, with an overall accuracy of 80%.

cDSSs built on the predictive capabilities of FarSight-aggregated and c-LDA classified modeling could demonstrate effective patient-centric and evidence-based risk assessment, thus ensuring proper channeling of preventive and prioritized care.


From Table 2, it can be observed that the value of p is always lower than the preset α of 0.05.

we conclude that the proposed model with the best performance is statistically significant than the other approaches and baseline methods with respect to all the employed performance evaluation metrics.


While the identities of the accounts might be available, the labels to learn from would not be present.

any supervised machine learning approach would fall short of being a fully automated solution to our initial problem.


Differently from troll accounts in social media, which usually have a high churn rate, news media accounts in social media are quite stable.

the latter can be used as an anchor point to understand the behavior of trolls, for which data may not be available.


In the distant-supervision scenario, we propagated labels from the media to the users.

in the latter case the user labels were only used for evaluation.


We can see that accuracy and macro-averaged F1 are strongly correlated and yield very consistent rankings for the different models.

henceforth we will focus our discussion on accuracy.


However, it has been shown that the quality of unsupervised CLWE is highly sensitive to the choice of language pairs and the comparability of the monolingual data (Søgaard et al., 2018).

these methods fail when the source language and the target language have very different linguistic structures (e.g.


It is possible that even for the essays where judges both agree that there is some kind of repetitive word use, they do not agree on what the repetition is.

we want to answer the following question: On the subset of essays where judges agree that there is repetition, do they agree on the same words as being repetitious?


Feedback about word overuse is helpful in terms of getting students to refine the use of vocabulary in their writing.

writing teachers would agree that it is an important capability in an automated essay evaluation system.


And Collins (2000) argues for "keeping track of counts of arbitrary fragments within parse trees", which has indeed been carried out in Collins and Duffy (2002) who use exactly the same set of (all) tree fragments as proposed in Bod (1992).

the major innovations of DOP are: 1. the use of corpus fragments rather than grammar rules, 2. the use of arbitrarily large fragments rather than restricted ones Both have gained or are gaining wide usage, and are also becoming relevant for theoretical linguistics (see Bod et al.


The probability of a parse tree T is the sum of the probabilities of its distinct derivations.

let tid be the i-th subtree in the derivation d that produces tree T, then the probability of T is given by DOP1 considers counts of subtrees of a wide range of sizes in computing the probability of a tree: everything from counts of single-level rules to counts of entire trees.


There are bk non-trivial subtrees headed by B@k, and there is also the trivial case where the left node is simply B.

there are bk + 1 different possibilities on the left branch.


We can create a subtree by choosing any possible left subtree and any possible right subtree.

there are aj= (bk+ 1)(ci + 1) possible subtrees headed by A @j. Goodman then gives a simple small PCFG with the following property: for every subtree in the training corpus headed by A, the grammar will generate an isomorphic subderivation with probability 1/a.


Each decision tree must be trained for the given language.

the decision trees produced for each language may differ in their target feature values or may require languagedependent feature extraction.


For example, for the insertion of constituents such as auxiliaries, prepositions, and infinitive markers, the value of the target feature is the citation form of the word being inserted.

for most of the models of stage 2 (fleshout), the definition of the target feature is language specific.


In order to enrich the English verb with the needed information, we combine pronouns and/or modals with following verbs and treat those combinations as 'new' fullform words in English.

we can obtain the information needed to select the correct verb form in the target language from one single English word.


The auxiliary 'do' does not carry information that is relevant when translating into Spanish or Catalan.

we can remove it from the sentence without harming the translation process (as described in (NieBen and Ney, 2001a) for the language pair German-English).


The corpus consists of transcriptions of spontaneously spoken dialogues.

the sentences often lack correct syntactic structure.


Such an approach requires large costs and a long evaluation time.

automatization of this method remains an important issue to solve.


The test set consists of 330 utterances in 23 conversations from the ATR bilingual travel conversation database (Takezawa, 1999).

this test set has different features from written language.


the translation capability of the language translation system equals that of an examinee with a score of around 700 points on the TOEIC.


However, utterances in our test set contain fewer words than in typical written language.

for the utterance unit evaluation, we conducted several experiments while varying N from 1 to 4 for BLEU and from 1 to 5 for NIST.


When choosing the form of representation of the corpus it was taken into consideration that it should comply with international standards.

the tag encoding system of the annotated Hungarian corpus was based on a technology (MSD) that has already been applied to other -mainly European -languages.


We assumed that a general-purpose dictionary (GPD) obtained from the PTB would be insufficient for handling biomedical literature.

we combined on-line glossaries to construct our own terminology dictionary, which we call the Medical Library Dictionary (MLD).


In the other three glossaries, the POS has not been defined.

our parser included out-of-vocabulary handling We supposed, however, that appropriate PoS would raise the performance we assigned PoS to these entries semi-automatically.


In fact, we are dealing with the so called state or complement clause.

corrective shuffling in this respect is inevitable.


Here let us concentrate on our approach to the generation of deep syntactic structure and how it satisfies the first two requirements.

the input to the MNLG is a node (i.e., feature structure) stipulating the syntactic type of the output (e.g., sen-tence: <s), semantics and further information on the current dialogue act in PROFIt various types of information are combined within one input node.


As can be seen in figure 2, wing "part of a bird" is closely related to tail, as is wing "part of a plane".

even after removal of the wing-node, the two areas of meaning are still linked via tail.


As will be described in section 4.1, on WVVW news sites in Japan, the number of articles updated per day is far greater (5 ,-30 times) in Japanese than in English.

it is much easier to find cross-lingually relevant articles for each English query article than for each Japanese query article.


The candidate term pairs can be reduced to about 3.5 ,-40% of their original sizes with the help of a pseudoparallel sentence pair, while about 37-50% of the correct bilingual term pairs found in the existing bilingual lexicon are preserved.

candidate reduction with the help of a pseudo-parallel 6 The computational complexity of bilingual term correspondence estimation based on contextual similarity in comparable corpora (sections 2 and 3.2.2) is much more than that based on pseudo-parallel corpus (section 3.2.1).


Putting all the clues together, the aligner comes up with the following alignment without actually having to know the two languages: the corridors korridorerna are jumping myllrar with av them dem However, looking at the sentence pair again, a second aligner with knowledge of both languages might realize that the verbs myllrar (English: swarm) and jumping do not really correspond to each other in isolation and that the expressions are rather idiomatic in both languages.

the second aligner might decide to link the whole expression "are jumping with" to the Swedish translation of "myllrar av".


Previous links point to the context from which they originated.

we can access any pair of features which is available for the context as well as for the linked items themselves.


Generally speaking, a single source expression can be translated into multiple target expressions.

a corpus contains multiple translations even though they are translated from the same source sentence.


For example, word-level statistical MT (Brown et al., 1993) translates a source sentence with a combination of word transfer and word order adjustment.

wordorder agreement is an important measure.


A literal translation means that source words are translated one by one to target words.

a bilingual sentence that has many word correspondences is literal.


On the other hand, in the case of Target 2, four words are found in the dictionary (Tt = 4), and there are three word links.

the TCR is *3 "' 0.67, and Target 1 is judged as more lit-5+4eral than Target 2.


The phrase (A-1 )S has low TCR, but the TCR of the noun phrase pair shinai' and 'the city' has 1.0.

the phrase (A-2)N P is generalized, and the long transfer rule (A-1 )S is generated from the non-literal translation.


The rules generated from literal translations are general, and they will be used for the translation of the other sentences.

by using the split construction, rules like templates are generated from non-literal translations and primary rules for transfer-based MT are generated only from literal phrases.


Since the variable x bound by the quantifier is shared by both scope and restriction, it is included in both the top feature structure of the determiner root node and the bottom feature structure of the determiner foot node.


If A and B are star-free languages, then we know that at least the following languages are star-free (Mc-Naughton and Papert, 1971): It is also possible that the language of a regular expression is star-free although the expression contains the Kleene star operator.

the method based on the properties of the syntactic monoid of the language is important.


These formalisms do not have the necessary expressive power to capture natural languages (which require at least mild context-sensitivity).

more expressive variants have been proposed, for example the multi-modal variant (MMCG) where applicability of postulates is controlled through the use of modal operators in the lexicon.


We prove that for any n e N+, [\E] hypo, 0 hypo2 H a With application of associativity and commutativity rules the resulting sequent can be rearranged so that all hypotheses occur in one minimal subsequent (for example, s o ( ((hypo and any permutation of this as well, by commutativity and associativity.

u{(s, a, ci + 1 )Perm I, 1 < < n} C L(Gn ), for any 72 E N+.


Using hypothetical reasoning and applying the Right Elimination rule i < n times, we can obtain i times the type a".

all remaining a's can be lifted to obtain it for any 71 N+, U{(s, a, 0 i + 1 )perm 0 n} C L(Gn ), and with the result for L(G 0 ), it follows that for any n E N, U{(s, a, o < < n} C L(Gri ).


The rules introduced above are all harmonic, or order preserving.

they are unhelpful when we consider sentences in which certain elements have "moved" with respect to their canonical position, such as in English heavy-NP shift, where an adverb comes between a verb and its object: (5) Ed saw briefly his old friend from Skye.


The typed slashes of CTL project their modes into structures and thereby limit or enable the application of structural rules to their local context.

contrary to CCG, there are no rule-constraints acting as absolute and global choices; instead, parametric options regarding the way in which expressions can be combined are selectively invoked via the appropriate category assignments in the lexicon.


The issue that now arises is how to integrate these modes with {*, o, x , .1: the latter modes control adjacency, and we would like to keep adjacency and dependency as separate (orthogonal) dimensions.

we propose to keep these dimensions as separate decorations on slashes as well, as follows: given dependency mode d, and adjacency mode a, we can form slashes \ a d versus / a d .


For instance, considering the translation "calle caner", a node for Spanish with feature "word:-2:calle", testing whether the word form at relative position -2 is "calle", will be translated into the node for Catalan "word:-2:carrer", which will test whether the -2 word is "caner".

we obtain models which are trained on Spanish and applied to Catalan text.


Looking at the results on the Catalan test (right block), all the models using the manual dictionary achieve a very competitive performance over 90% of F1 measure.

the techniques to adapt a NER model to Catalan seem to work considerably well.


The constraints on mapping semantic arguments to syntactic positions reflect underlying semantic properties of the verbs (Pinker, 1989;Levin, 1993).

alternations, such as those in (1) above, in which the same argument (hay or truck) is mapped to different positions, are particularly useful in distinguishing verb classes.


Moreover, instead of adopting the Speech Grammar slot-filling paradigm for semantic interpretation, our grammar builds a sophisticated, compositional semantics involving A terms which are passed as the value of a single slot for the recognised sentence.

the output of the speech recognition stage is a semantic representation, and no further parsing is required before handing it over to the dialogue manager.


Secondly, our data clearly indicates that in many cases, the choice of the substitution phoneme/allophone is related to the phonetic or graphemic surroundings of the substituted phoneme.

in order to restrict their application to appropriate contexts, most rewrite rules require context conditions on the phoneme level and/or on the orthographic level (see below).


Syntactic patterns can help here; if looking for collocates for a noun, for example, it makes sense to collect any modifiers of the word in question, and any words participating in prepositional constructions.

if a sense of the entry for breach has the example sentence She was guilty of a breach of trust.


In addition, the corpus-based processes of translation, evaluation, and paraphrasing have synergistic effects.

we are optimistic about the further progress of components and their integration.


This test states that every string of words that can be placed at the start of a finite main clause, forms a single constituent.

if and only if a [NP PP] or [PP PP] sequence can be fronted, it forms a single NP containing a noun-attached PP.


Although it is not the best in all respects, we prefer this method, because it uses cooccurrence strength values for adjacent nouns and prepositions in the order in which they appear in the text (see §3.2), this in analogy with the fact that order is meaningful in PP attachment.

we added the Cooc(N P) feature as the eleventh feature to our data sets for both corpora.


Within the set of lexical features, the bigram features were sparser than the unigram features, and neither of the algorithms benefited from the bigram features.

given the current data set, all necessary information was available in the four unigram features in combination with the cooccurrence feature.


One reason for this is that the distribution of word categories differs considerably between the two corpora: Our sentences contain ten times as much foreign-language material (Fm) as the tagger's training set, and also more names (NE), both of which are (in German) very difficult to distinguish from proper nouns.

in many cases the information provided by the tagger is actually misleading.


Moreover, LoPar does not depend on the availability of external tagging information.

the net contribution of the chunker without an additional tagger could also be determined, a configuration which was not available so far.


However, overall accuracy does not characterize how the classifier performs on each individual category.

to measure classifier performance for each category we use the precision, recall, and macro-average F-measure scores.


This compares favorably to the best accuracy reported in (Stamatatos et al., 2000) of 72% on group A and 70% on group B.

1 our accuracy improvement is 2% on group A and 18% on group B, which is surprising given the relative simplicity of our method.


For SVO and OV languages, we assume that the trailing sequence of verbal dependents that are realized in canonical order at the clause level are assigned T, while all preceding ones are considered to be CB.

we are mostly interested in the rightmost violation of SO among the dependents of a given verbal head.


Our principles predict that a referent with a pitch accent is NB, while a referent violating SO is CB -both cannot be simultaneously the case.

in general a dependent that appears sentence-initial, and which receives pitch accent, must fill a semantic role that is leftmost in its head's SO.


Our contribution is two-fold: first, our principles derive evidence of CB/NB-ness in the 4valued Boolean lattice, thus supporting both underspecification by lattice top T and easy combination by lattice meet n; second we have shown that our formulation naturally fits in the concurrent constraint approach of TDG.

we have access to practically efficient constraintbased parsers, and we take advantage of the fact that multiple sources of structural indications can simultaneously influence the realization of information structure.


For our approach, we determine the corresponding formal target language concept for each word of a natural language input.

we distinguish whether a word is an initial or a non-initial word of a concept.


But as their information needs and sophistication grow, so does their frustration with simple keyword search.

seemingly obscure features, such as boolean searches, wildcards, and topic classification become increasingly relevant to them.


A possible source is that SABLE annotated input is additionally processed and possibly modified by applying Mary defaults in ways we cannot control.

mary may sometimes "improve" the SABLE intonation specification (towards default).


The likelihood of a document is given by the total probability Of course, the true parameters 0 of the mixture model are not known.

one estimates the parameters from labeled training documents, i.e.


Note that P(d18) is the same for all classes, thus d can be classified by computing Cd = argmax P(ej 0)P(d cj; 0) (4) ct EC The multi-variate Bernoulli event model assumes that a document is generated by a series of VI Bernoulli experiments, one for each word wt in the vocabulary V. The outcome of each experiment determines whether the corresponding word will be included at least once in the document.

a document di can be represented as a binary feature vector of length I V. where each dimension t of the vector, denoted as Bit c {0, 1}, indicates whether word wt occurs at least once in dz .


The parameters = P(w t, ci ; 0) of the mixture component ci can be estimated as the fraction of training documents in cj that contain w 1 : 1 6tvtle; P(wt Icj; 0) = Bit P(ci di ) The multinomial event model assumes that a document di of length di is generated by a sequence of I d7 word events, where the outcome of each event is a word from the vocabulary V. Following McCallum and Nigam (1998), we assume that the document length distribution P(Id, I) does not depend on the class.

a document di can be represented as a vector of length I V , where each dimension t of the vector, denoted as Nit > 0, is the number of times word w t occurs in di.


The Ling-Spam corpus consists of 2412 messages from the Linguist list 3 and 481 spam messages.


However, such cost-sensitive measures are problematic with a Naive Bayes classifier because the probabilities computed by Naive Bayes are not reliable, due to the independence assumptions it makes.


In this case, the latter action (sentence) occurs after the former.

a pair of actions (C%-l-qh%4 Vklz6A--4-6 (police finds suspect)'), (`Sc Vtti--(police arrests suspect)')) can be extracted.


When two verbs have this relationship, they tend to be in time order.

in this case a pair of actions can be extracted.


In the above example, the verb 16R1-L (find)' modifies the noun V-MAs (suspect)', and V.RA-(suspect)' is the object of Altt The verbs(`C VI-(trust)' and ta (visit)') in the relative clauses of both the sentences are in the present tense, and the actions in the relative clauses do not occur before the actions in the main clauses.

we extract a pair of actions only when the verb in the relative clause is in the past tense.


Passive sentences also cause a problem, since passivization often changes the case of the verb.

we generated patterns where the case of the verb changes to obtain passive cases from the active ones.


First, we generalized the extracted pairs by changing subjects and objects into semantic features and merging similar verbs into one by using Japanese thesaurus `Bunrui Goi Hyo' (NLRI, 1964).

of this generalization, we could easily determine whether two pairs are same and count the frequency of occurrence.


Here let us concentrate on our approach to the generation of deep syntactic structure and how it satisfies the first two requirements.

the input to the MNLG is a node (i.e., feature structure) stipulating the syntactic type of the output (e.g., sen-tence: < s), semantics and further information on the current dialogue act in PROFIt: 2  various types of information are combined within one input node.


For each of them several alternative algorithms will be implemented and the best performing combination will be included in the final system.

of this project several tools for Romanian will be developed.


Unlike traditional "bag of words" techniques and other methods for extracting syntactic relations from captions for indexing pur-poses, our prototype extracts meaning representations that capture pragmatic relations between objects depicted in the photographs.

most of the complexity of the written text is eliminated, while its meaning is retained in an elegant and simple way.


In the source domain it can be inferred, from the literal meaning of the utterance, that psychoanalysis (qua house) physically contains the main house, the annexes.

by the containment mapping relationship, psychoanalysis is being claimed in reality to contain corresponding items.


We further assume that when a person's mind is being viewed as a physical space, the person's conscious self is viewed as a person physically located in (the main part of) that space.

if the idea is physically located in the far-reaches of the mind-space, whilst the conscious self of Anne is located in the main part of the mind-space, then the conscious self will be able to operate physically to only a very limited degree on the idea.


Since speakers can always add phrases after the predicate, grammatical completion is necessary but not sufficient to make a cTRP.

linguistic theory suggests that knowing where to find TRPs will help one know where to place backchannel continuers, and that pragmatics, grammar and intonation are all useful cues.


Moreover, the multimodal features considered by Denny seemed too far from the current stateof-the-art in speech recognition systems to be of immediate practical interest.

for this work, we restrict ourselves to pause duration and part-of-speech tag sequences as inputs.


We decided to approximate syllables by words.

from each of our Follower backchannels, we can measure the number of words back to the last Follower backchannel continuer, or Giver TRP, as determined by move boundaries.


In contrast to the Web, WordNet focuses on the lexical knowledge fabric by unearthing the "synonymous" terms.

to overcome the QA gap at the lexical and syntactic levels, QUALIFIER looks up WordNet to fmd words that are lexically related to the original content words.


If q(1) does not return sufficient number of relevant documents, the extra terms added is reduced and the Boolean search is repeated.

we successively relax the constraint to ensure precision.


The estimation of the concept frequencies in (7) relies on the simplifying assumption that a given noun is equally likely to be represented by any of its conceptual classes.

the occurrence frequency of a compound is evenly distributed across all possible concept combinations representing the nouns forming the compound, since we cannot assess (without access to a corpus annotated with class information) which concept combinations are likely and which are not.


Special emphasis in the development of fsq is layed on providing users a very powerful query language.

full first-order logic was chosen as query language which allows arbitrary quantifications over nodes in a tree.


One of the design decisions for the development of the treebank was the commitment to reusability.

the choice of the syntactic annotation scheme should not reflect a particular syntactic theory but rather be as theory-neutral as possible.


Secj is the secondary relation representing refl, (tok x T) true, if a(x) E T.] where Tj is the predicate representing token T, Complex formulae are interpreted classically.

a query or closed formula is true or false of a tree.


If checked, the next query will not be executed on the whole corpus chosen but rather on the previous search result for that corpus.

the user can first pose an approximating query, inspect the result and then formulate a more fine grained query to be executed only on the result of the first query to get a smaller answer set.


fsq is designed to provide users a very expressive query system.

its query language is full first-order logic, and its underlying data structures are finite first-order structures.


The IL-5 also interacts with a series of nuclear receptors including retinoic acid receptor (RAR), thyroid hormone receptor (TR), and orphan nuclear receptors (hepatocyle nuclear receptor 4 (HNF4) and constitutive androstane receptor (CAR)] However, IL-1 does not interact with an orphan nuclear receptor known to antagonize ligand-dependent transactivation of other nuclear receptors.

we defined the following unary tag operations: -similar(TERM), which denotes a set of terms belonging to the same cluster as TERM; following(TAG), which denotes an entity which follows (not necessarily immediately) the given TAG; preceding(TAG), which denotes an entity which preceedes (not necessarily immediately) the given TAG, and -range (TAG, in, n), which denotes an entity which appears in a window of m words left and n words right of the given TAG.


l Alterf can be downloaded from SourceForge at http://sourceforge.net/projects/leonlp/ (Wavfile) (Atoms) Transcriptionwhere (Wavfile) is the name of a file containing speech data, (Atoms) is the intended semantic representation, and (Transcription) is a text transcription of the speech data.

a typical line might be utt12.wav next_line go on Training can be carried out in either speech or text mode.


In general, we assume that the parse output will be quite distinct from the semantic representation we are ultimately interested in producing, and is more likely to be a linguistically motivated form like a parse tree or a predicate-argument structure.

for example, with the grammar used for the experiments in Section 4 the parse output for go to the next step is [ [ form ( imperative, [ [go, term (pron, you, [ )  We implement hand-coded rules defining patterns which match sub-structures of these parse representations: each such rule has the form pattern((Pattern), (Atom), (Example)).


Neither of these two restrictions apply to adjuncts, as illustrated in the examples below.

a) *Chris rented the gazebo to girls, to boys b) Kim met Sandy in Baltimore in the hotel lobby in a corner the probability of a PP being an adjunct can be approximated as the probability of its occurrence in second position in a sequence of PPs, as indicated in (2).


NP arguments cannot be paraphrased by a copular relative clause (examples b and b'), while adjuncts can (examples a and a').

a) a man from Paris a') a man who was from Paris b) the weight of the cow b') *the weight that was of the cow the probability that a PP is an adjunct can be approximated by the probability of its occurrence following a copular verb, such as be, become, appear, seem, remain (Quirk et al., 1985), as indicated in (3).


Then, the test examples classified as verbs are given to the verb argument-adjunct classifier, and the test examples classified as nouns are given to the noun argument-adjunct classifier.

this cascade of classifiers performs the same task as the 4-way classifier, but it does so in two passes.


This leads to the following evaluation goal: Find all instances of word pairs that occur in a specific (morpho-)syntactic relationship in the source corpus.

our evaluation is based on instances of candidate pairs, i.e.


First, categorial change in Chinese words is not often associated with morphological marking.

the same word form can have more than one syntactic categories, and this difference is not marked by any derivational affixes.


Any categorial shift undergoes a transition, moving from the "word play" end toward the genuine ambiguity end.

we distinguish three levels of ambiguity, namely: • Regular ambiguity: a word has multiple POSs which are well accepted and described in any existing lexicon, as in (4).


Nevertheless, it is the process of transition that is of linguistic interest, and the usage frequencies from corpus data gathered over time would give good evidence for it.

when tagging a corpus, the different uses of the same word must be sufficiently represented to enable this kind of longitudinal empirical study to be carried out.


Then all classes that contained less than 5 nouns were discarded.

the BNC nouns formed 233 classes with 1807 unique nouns and the AP nouns formed 137 classes with 816 unique nouns.


Today, in particular for the French language, the developers face the great diversity of the offer in the domain.

the need for a complete comparative evaluation framework -including a pivot annotation formalism, a reference treebank, evaluation metrics and the associated software -is increasing.


It is worth noting that most of the recently developed parsers use a robust approach.

they do not always produce a complete parse of the sentence, but they are able to produce a result, whatever the size, the particularities and the grammaticality of the input.


In a dependency structure, every word token (dependent) is related to another token (head) over a grammatical role, but for one word token, which is called the root.

a parser constructing a dependency structure needs to associate every word token either with a head token plus grammatical role or mark it as the root or 'TOP' node.


An important objection is that the weight of words is determined quite arbitrarily (Clark and Hockenmaier, 2002).

we adopt Lin's scheme with the above provisos.


In the categorial grammar tradition initiated by Lambek (1958), the standard way of providing a semantics for category notation defines the denotation of a category description as a set of strings of terminal symbols.

assuming an alphabet Σ and a denotation function [[.


As a category, it has one direct parent Planets of the Solar System, which in turn belongs to two more general categories, Planets and Solar System.

categories form a directed acyclic graph, allowing multiple categorization schemes to co-exist simultaneously.


Cimiano and Völker evaluate different context features and prove that syntactic features work best.

in our experimental settings we considered only such features extracted from a corpus parsed with a dependency parser.


We think, that the variation in performance between categories is due to the different specificity of their textual contexts.

some classes tend to co-occur with more specific syntactic features, while for other classes this is not true.


In SMT, every English sentence e is considered as a translation of a given French sentence f with probability P r (f |e).

the problem of translating f can be viewed as a problem of finding the most probable translation of f : e * = argmax e P r(e|f ) = argmax e P r(f |e)P (e).


We note that P (f , a|e) = n i=1 1 2 1−δ(φ i ,0) = 2 −|C | .

viterbi alignment results in the minimum cover for X.


e n where each e i and f j is distinct and set the Model 3 parameters as follows: Clearly, with the above parameter setting, P (f , a|e) = n j=1 M j, a j if a is a permutation and 0 otherwise.

thus, by construction, PROBABILItY-3 computes perm (M).


Clearly, all alignments can be encoded using at most p (| (f , e) |) bits.

if (f , e) computes P (f |e) and hence, PROBABILITY-3 is in #P. It follows immediately from Lemma 2 and Lemma 3 that Theorem 1 PROBABILITY-3 is #P-Complete.


Lemma 7 (j, i, m, l)-COUNT-3 is in #P. Proof: Similar to the proof of Lemma 5. times in e. Replace these k occurrences by only one occurrence of e i and connect all the words connected to them to this word.

this would increase the score of e by a factor of 2 k−1 > 1 contradicting the assumption on the optimality of e. the only candidates for e are subsets of {e 1 , e 2 , .


We then set (1) (2) i and N j,i = 1 0 otherwise n (φ|e) = 1 φ = 1 0 otherwise Now, P f |e (1) = perm (M), and P f |e (2) = perm (N ).

given the output of E-DECODING-3 we can find out which of M and N has a larger permanent.


Computing the error measures on these sentences only, we observed significant improvements in WER and PER and a small improvement in BLEU with respect to the original translations.

the quality of previously unseen consensus translations as generated from the original translations is acceptable.


In the IWSLT 2004 evaluation, the English reference translations for the Chinese-English and Japanese-English test corpora were the same, except for a permutation of the sentences.

we could combine MT systems which have different source and the same target language, performing multi-source machine translation (described e. g. by (Och and Ney, 2001)).


• A test corpus of translations generated by each of the systems is used for the unsupervised statistical alignment training.

the decision on how to align two translations of a sentence takes the whole document context into account.


An important feature of a real-life application of the proposed alignment technique is that the lexicon and alignment probabilities can be updated with each translated sentence and/or text.

the correspondence between words in different hypotheses and, consequently, the consensus translation can be improved overtime.


On the other hand, we did not want our system to be dependent on this type of human-added information.

we decided to do several setups which made use of this information to various degrees.


For the coreference resolution system that this P R F % Correct None 71.9 % 55.1 % 62.4 % 75.1 % Sentence Breaks 70.1 % 57.7 % 63.3 % 74.9 % Interruption Points 80.0 % 60.9 % 69.2 % 79.6 % Both 74.2 % 60.4 % 66.6 % 77.3 % Table 3: Results of Automatic Classification Using Various Information Sources work is part of, only the fully automatic variant is an option.

future work must try to improve its recall without harming its precision (too much).


This is not a convenient size for recognition as it will affect accuracy and speed.

we tried to find an optimal vocabulary combining the small MP3 vocabulary of around 300 words with a smaller part of the GSLC vocabulary and the newspaper vocabulary.


To address the problem of user confidence, we also briefly summarize options that the user model determines to be irrelevant (see section 4.3).

we give users an overview of the whole option space, and thereby reduce the risk of leaving out options the user may wish to choose in a given situation.


Obviously, it is not possible to present all of the options and tradeoffs represented in the tree in a single turn.

it is necessary to split the tree into several smaller trees that can then be presented over several turns.


Even in freer-word order languages such as Czech, almost all non-projective dependency trees are primarily projective, modulo a few nonprojective edges.

if our inference algorithm starts with the highest scoring projective parse, the best non-projective parse only differs by a small number of edge transformations.


Additionally, the likelihood values across different initializations are not comparable, as we will show.

the likelihood function computed over the training data cannot be used as a predictor of model performance across different models.


The PLSA model (Hofmann, 1999) is a generative statistical latent class model: 1 , where The joint probability between a word and document, , is given by and using Bayes' rule can be written as: The likelihood function is given by  An important consideration in PLSA modeling is that the performance of the model is strongly affected by the initialization of the model prior to training.

a method for identifying a good initialization, or alternatively a good trained model, is needed.


In addition, performance using random initialization was never worse than kmeans initialization, which itself is sensitive to initialization values.

in the rest of our experiments we initialized PLSA models using the simpler random-initialization instead of k-means initialization.


On the other hand, if the SST space contains too many irrelevant features, overfitting may occur and decrease the classification accuracy (Cumby and Roth, 2003).

the fewer features of the ST approach may be more appropriate.


When the productions associated with n 1 and n 2 are different, we can avoid to evaluate ∆(n 1 , n 2 ) since it is 0.

we look for a node pair set where p(n) returns the production rule associated with n. To efficiently build N p , we (i) extract the L 1 and L 2 lists of the production rules from T 1 and T 2 , (ii) sort them in the alphanumeric order and (iii) scan them to find the node pairs time, but, if p(n 1 ) appears r 1 times in T 1 and p(n 2 ) is repeated r 2 times in T 2 , we need to consider r 1 × r 2 pairs.


In that case, even if association values from all pairs are used, evidence from less-reliable, low-strength pairs will contribute little to the final cumulative evidence, as compared to more-reliable, high-strength pairs.

even if the base WCCM is less accurate when generated from untagged text, it can still be used to provide association values suitable for most natural language applications.


For each occurrence t ¼ of a target word t, let T ¼ be the set of words (tokens) co-occurring within a predetermined window around t ¼ ; let T be the union of all such T ¼ and let t be the set of all such T ¼ .

(t is equal to the number of occurrences of t, and T is equal to the total number of words (tokens) in the windows around occurrences of t.) We describe 1 Measures of association (Sheskin, 2003)  D I W is based on the assumption that the more dominant a particular sense is, the greater the strength of its association with words that co-occur with it.


We then generate different test data sets d α from the bin, where α takes values 0 1 2 1, such that the fraction of sentences annotated with s 1 is α and those with s 2 is 1 α.

the data sets have different dominance values even though they have the same number of sentences-half as many in the bin.


all three measures will perform similarly in case the co-occurring words give an unweighted vote for the most appropriate sense of the target as in D I U and D E U .


The average number of sentences per target word in each dataset d α is 323.

the results shown above correspond to an average target text size of only 323 sentences.


In each step of the clustering algorithm, the words from the triplets and the features are merged, if the overlap factor similarity measure (Curran, 2003) found them to be similar enough (over 80% overlapping words out of 200).

if the element (space, NASA, Mars) : (orbital, satellite, astronauts,...) and (space, launch, Mars) : (orbit, satellite, astronaut, ...) were found to be similar, they are merged to (space=2, NASA=1,Mars=1,launch=1) : (orbital=1,satellite=2,orbit=1,astronaut=1,...).


This, in fact, is very similar to the WSI task, which is supposed to sort the set of words apart that co-occur with the target word and refer to its different meanings.

again it is possible to take two words, view their co-occurrences as one set and let the WSI algorithm sort them apart.


In Figure 6, we see that in all three valid structures, "quickly" is attached higher in the tree than "here".

of this, no combination of inversions can bring "quickly" between "here" and "ran".


Whereas documents in a bilingual corpus are typically very closely aligned, monolingual corpora exhibit a much looser level of alignment, with similar content expressed using widely divergent wording, grammatical form, and sentence order.

many of the simple surface-based methods that have proven to be so successful in bilingual sentence alignment, such as correlation of sentence length, linearity of alignment, and a predominance of one-to-one sentence mapping, are much less likely to be effective for monolingual sentence alignment.


Unfortunately, plain word-overlap assigns all words equal importance, not even distinguishing between function and content words.

once the overlap threshold is decreased to improve recall, precision degrades rapidly.


Regarding hearers' roles in meetings, we are focused only on ratified participants.

the problem of addressee identification amounts to the problem of distinguishing addressed from unaddressed participants for each dialogue act that speakers perform.


Since there are only a few instances of subgroup addressing in the data, we removed them from the data set and excluded all possible subgroups of meeting participants from the set of class values.

we define addressee classifiers to identify one of the following class values: individual P x where x ∈ {0, 1, 2, 3} and ALLP which denotes the whole group.


(b) Instancy of error detection: A dialogue participant observes his dialogue constantly and actions presenting strong evidence (Level 3).

when there is an error, the commander detects it immediately once an action related to that error occurs.


The results of the study confirmed the predicted effect and, moreover, provide a classification of clarification question types.

the particular kinds of analysis found to initiate insertion sequences in HHC situations are clearly active in HCI clarification questions as well.


If a user already has a strong membership analysis in place-for example, due to preconceptions concerning the abilities (or, more commonly, lack of abilities) of the artificial agent-then this influences the design of that user's utterances throughout the dialogue.

we have been able to define distinctive linguistic profiles that lead to the identification of distinct user groups that differ reliably in their dialogue strategies, particularly in their initiation of subdialogues.


In the latter case, communication will either fail or invoke rechecking of membership categories to find a basis for understanding (e.g., 'do you know what cups are?').

what can be seen on the part of a user as reducing the complexity of a task can in fact be removing information vital for the artificial agent to effect successful interpretation.


However, we think this approach lacks realism, since it is also true that, for the very same languages, there is no available lexical resource where terms are tagged as having either a Subjective or an Objective connotation.

the availability of an algorithm that tags Subjective terms as being either Positive or Negative is of little help, since determining if a term is Subjective is itself non-trivial.


If either of the two words in a pair in the test dataset does not appear in the training dataset, we excluded the pair from the test dataset since the problem of unknown words is not in the scope of this research.

we evaluate the pairs that are not in the training dataset, but whose component words appear in the training dataset.


Second, the membership of these central words in the category is less ambiguous than the membership of more peripheral words.

we can estimate the centrality of a word in a given category in two ways: 1.


These results suggest that the two measures of word centrality, Net Overlap Score based on multiple STEP runs and the inter-annotator agreement (HM vs. GI-H4), are directly related 7 .

the Net Overlap Score can serve as a useful tool in the identification of core and peripheral members of a fuzzy lexical category, as well as in predic-tion of inter-annotator agreement and system performance on a subgroup of words characterized by a given Net Overlap Score value.


As we observed in the introduction, obtaining a sample of unbiased documents is not the same as obtaining an unbiased sample of documents.

we will not motivate our method in terms of whether it favors unbiased samples from the Web, but in terms of whether the documents that are sampled appear to be balanced with respect to a set of deliberately biased samples.


At the top of the rankings we also find other less narrowly topic/genre-dependent categories such as "W" for mode, or "W miscellaneous" and "W pop lore" for genre.

the hypothesis seems supported by these experiments.


The progressive increase in recall given by the increasingly looser translations is in inverse proportion to the decrease in precision, which hints that we should give precedence to the alignments obtained with the more accurate methods.

we start by adding the alignments in FJ to the output set.


Web documents are authored by people with a widely varying command of English and their pages are not as carefully edited as newspaper articles.

we anticipate a significantly larger number of misspellings and typographical errors.


The number of blocks in a sentence is equal to the number of gaps among the blocks plus one.

the block movements can equivalently be expressed as long jump operations that jump over the gaps between two blocks.


The calculation of D(i, l) requires all values of D(i , l) to be known, even for i > i.

the calculation takes three steps for each l: 1.


This means that the path from (0, 0) to (i , l) is less expensive than the path from (0, 0) to (i , l).

the path from (0, 0) through (i , l) to (i, l) is less expensive than the path through (i , l).


Therefore, the remaining candidate words (at least I − L) must be covered by deletion edges.

this means that at least I − L deletion edges will be found in any d LJ path, which leads to the length difference between the two sentences gives us a useful miscoverage penalty lp len : this penalty is independent of the d CD alignment path.


Thus, an optimal d CD alignment path is optimal for d CD + lp len as well.

the search algorithm in Section 3.2 will find the optimum for this sum.


We can use the absolute miscoverage as a penalty lp misc for d CD : This miscoverage penalty is not independent of the alignment path.

the proposed search algorithm will not necessarily find an optimal solution for the sum of d CD and lp misc .


On the other hand, replacing the same word with a completely different one probably will.

it seems advisable to make substitution costs dependent on the semantical and/or syntactical dissimilarity of the words.


On the other hand, the common prefix length is sensitive to critical prefixes such as "mis-" for the same reason.

the common prefix length, normalized by the average length of both words, gives a reasonable measure for the similarity of two words.


But only the latter allows for an efficient calculation of confidence intervals.


The brevity penalty is calculated as: where c is the length of the corpus of hypothesis translations, and r is the effective reference corpus length.

1 the Bleu score is calculated as A Bleu score can range from 0 to 1, where higher scores indicate closer matches to the reference translations, and where a score of 1 is assigned to a hypothesis translation which exactly matches one of the reference translations.


The number of permutations varies with respect to sentence length and number of bigram mismatches.

as a hypothesis translation approaches being an identical match to one of the reference translations, the amount of variance decreases significantly.


This problem is made worse by the fact that Bleu equally weights all items in the reference sentences (Babych and Hartley, 2004).

omitting content-bearing lexical items does not carry a greater penalty than omitting function words.


The rule-based entry tagger relies on font style, which can be also incorrect.

we also investigate whether improving font style accuracy will further improve tagging results.


Moreover, although each dictionary has hundreds of pages, it is not feasible to use a significant portion of the dictionary for training.

the training data should be small enough for someone to annotate ground truth in a short amount of time.


For example, ago has the ambiguity class IN/RB, and in example (5a), it resolves to RB.

following the notation in Pla and Molina (2004), we assign ago the complex ambiguity tag <IN/RB,RB> in the training data, as shown in (5b).


For the corpus used as training data in the experiments, the probability of a potential topic boundary being an actual one is approximately 2.2% for all subtopic segments, and 0.69% for top-level topic segments.

the Monte Carlo simulation algorithm predicts that a speaker turn is a segment boundary with these probabilities for the two different segmentation tasks.


In order to facilitate meeting browsing and question-answering, we believe it is useful to include subtopic boundaries in order to narrow in more accurately on the portion of the meeting that contains the information the user needs.

we performed experiments aimed at analysing how the LM and CM segmentation models behave in predicting segment boundaries at the two different levels of granularity.


• If x is not a keyroot, then there exists a node z such that : x < z < pos 1 , z is a keyroot and L(z) = L(x).

x belongs to the leftmost branch of T [L(z), .


Then D(s 1 , s 2 ) = 2.5 and, similarly D(s 1 , s 2 ) 2.67.

the normalized degree of parallelism is D(S 1 , S 2 ) = 2 5+6 × 2.67, which is about 0.48.


Green and Oakes show significantly more parallelisms than the other texts.

if we consider a lazy method that would put all pairs in the class of parallelisms, Green and Oakes will yield a priori better results.


For all other states, frustration occurs with each of them so infrequently 2 that the resulting states appeared less than the our threshold of 50 instances.

these 5 frustration states are grouped together in the "threshold state" and our MDP found that the best policy when in that state is to give no feedback.


The primary problem with this model is that most of the model features encode properties related to including or dropping constituents from the tree with no encoding of bigram or trigram surface features to promote grammaticality.

the model will sometimes return very short and ungrammatical compressions.


We simply augment the dynamic programming table and calculate C[i] [r], which is the score of the best compression of length r that ends at word x i .

this table can be filled in as follows if we require a specific compression rate, we simple determine the number of words r that satisfy this rate and calculate C[n] [r].


A possible direction of research is to investigate multilabel learning techniques for structured data (Mc-Donald et al., 2005a) that learn a scoring function separating a set of valid answers from all invalid answers.

if a sentence has multiple valid compressions we can learn to score each valid one higher than all invalid compressions during training to avoid this problem.


opinions), as opinions on the same entity feature may be uniform or varied.

summarizing a corpus of evaluative text is much more similar to summarizing conflicting reports than a consistent set of factual documents.


We found from early experimentation that the most informative sentences could be accurately determined by examining the extracted CFs.

we created our own sentence-level feature based on the number, strength, and polarity of CFs extracted for each sentence.


However, they would not naturally be clustered together by a human observer, because grouping of objects also needs to take into account the position of the surrounding elements.

while e 1 is as far away from e 2 as e 5 is from e 6 , there are elements which are closer to {e 1 , e 2 } than to {e 5 , e 6 }.


The first is that the contents of the DoK-MIE come from (third-party) human annotations and are thus trusted.

we take them as primary and use them to scrub the WordNet synsets.


For Learner B, we use phrasal/expression verbs and overlap as indicators to remove problematic synsets.

we avoid accidentally contaminating the nonliteral set.


We decided which clustering parametrisations should be tried on the test sets based on the notion of stability: Both algorithms increase the number of clusters by one at each iteration.

each parametrisation yields a series of cluster configurations as the number of iterations increases.


We base our standard role labelling system on the SVM labeller described in Giuglea and Moschitti (2004), although without integrating information from PropBank and VerbNet for FrameNet classification as presented in their paper.

we are left with a set of fairly standard features, such as phrase type, voice, governing category or path through parse tree from predicate.


For example, there are many cases where SBAR and S nodes are used interchangeably in the MSA treebank.

an S clause headed by a complementizer is converted to an SBAR.


Hence, the goal is to skew the distributions of the SVO constructions in the MSA data.

vSO constructions are both replicated and converted to SvO constructions.


However, in the LA data, lys occurs only as a particle.

we convert all occurrences of lys into RP.


This synchronous grammar can be used to parse new dialect sentences using statistics gathered from the MSA data.

this approach can be thought of as a variant of the treebank-transduction approach in which the syntactic transformations are localized to elementary trees.


For trees recording derivations in context-free grammars, there are exactly as many substitution operations as nonterminals on the right-hand side of the rule used.

child order in the derivation tree can be used to record the identity of the substitution node.


We will write B(I, O) where I and O characterize a subclass of homomorphisms for the set of bimorphisms for which the input homomorphism is in the subclass indicated by I and the output homomorphism is in the subclass indicated by O.

b(D, M) is the set of bimorphisms for which the input homomorphism is a delabeling but the output homomorphism can be arbitrary.


Separately, the relation between context-free tree grammars and macro tree transducers has been described, where the relationship between the monadic variants of each is implicit.

taken together, an equivalence between the tree-adjoining languages and the image languages of monadic macro tree transducers might be pieced together.


, x n )) = α and if α is an initial tree, construct the equation where the right-hand-side transformation • is defined by 4 (2) Note that the equations are linear and complete, because each variable x i is generated once as the tree α is traversed, namely at position π i in the traversal (marked with i ), and the variable x 0 is generated at the foot node only.

the generated embedded tree transducer is linear and complete.


As a candidate may belong to multiple clusters, we obtain: Given that a word appears, we assume that it has the same probability to appear in a context as all other words in the same cluster.

: We can now rewrite the equation in (2) as: This equation splits our model into two parts: one models which clusters a word belongs to and the other models how appropriate a cluster is to the question contexts.


Characteristic anchor-sets are assumed to uniquely identify a specific event or fact.

any template that appears with such an anchor-set is assumed to have an entailment relationship with the input template.


In this example Var1 matches the verb 'activate', Word matches the verb 'detect' and the syntactic relations for Word are mapped to the ones for Var1.

we can infer that the subject and object relations of 'detect' are also related to 'activate'.


For example, in the sentences containing the phrase 'X bind specifically to Y' the parser consistently attaches the PP 'to' to 'specifically' instead of to 'bind'.

the template 'X bind to Y' cannot be directly matched.


A semantic predicate, such as like v 1, is a logical predicate and as such is expected to have the same arity wherever it occurs in the DELPH-IN grammars.

models for an MRS may be defined in a language with or without argument labels.


PPs which are not modifier-like are either scopal (small clauses) or NP-like (case marking Ps) and free-scoping.

somewhat counter-intuitively, we can select the head EP from the set of EPs which share a label by looking for an EP which has no argument EPs in that set.


However, because this ltop is not equated with any label, there is no direct link between the verb and the determiner.

the noun will have a link from the determiner and from the verb.


is combined with the gap filler, the label equality and the argument instantiation correspond to different EPs.

there is a label equality which cannot be combined with an argument link and has to be represented by an undirected /EQ arc.


The 4-gram back-off target LM is trained on the English part of the bitexts and the Gigaword corpus of about 3.2 billion words.

it is likely that the target language model includes at least some of the translations of the French Gigaword corpus.


Our motivation for this approach was to be able to improve SMT performance by 'creating' parallel texts for domains which do not have enough or any parallel corpora.

only the news-  Figure 5: BLEU scores when using newscommentary bitexts and our extracted bitexts filtered using TER.


The samples employed to build the dictionary were gathered from different sources such as interviews, adolescents' descriptions of their emotions and university students' essays.

the 8742 word dictionary is broad and avoids bias from any one particular source.


What differentiates one from the another is the imagery score, which is higher for the former.

value of the norm is lower for goodies than for good.


The resulting small quantity of training data may be the reason for bad performance.

we decided to back off to only binary n-gram features as part of our feature vector.


Note that monosemous words will be related to just one concept, whereas polysemous words may be attached to several.

of the disambiguation process, every concept in Concepts i , i = 1, .


The disambiguation graph G D is then just the union of the vertices and edges of the shortest paths, The disambiguation graph G D is thus a subgraph of the original G KB graph obtained by computing the shortest paths between the concepts of the words co-occurring in the context.

we hypothesize that it captures the most relevant concepts and relations in the knowledge base for the particular input context.


As the words are linked to the concepts by directed edges, they act as source nodes injecting mass into the concepts they are associated with, which thus become relevant nodes, and spread their mass over the LKB graph.

the resulting personalized PageRank vector can be seen as a measure of the structural relevance of LKB concepts in the presence of the input context.


it must respect the following properties: As the objective function is linear with respect to X and as the constraints that X must respect are linear equations, we can solve the clustering problem using an integer linear programming solver.


in practice, we use heuristics for dealing with large data sets.


In general terms, we can assume that nbitr << |CLI|, but not κ max << |CLI|.

in the worst case, the algorithm has a O(κ max × |CLI|) computation cost.


We suppose in this context that many NEs in NE are already annotated.

under this assumption, we have in each cluster provided by the CBC system, both annotated and non-annotated NEs.


We want to combine these two systems when annotating NEs occurrences.

we resolve any conflicts by applying the following rules: • If the same NE occurrence has two different annotations from the two systems then there are two cases.


At this stage, we want to build the NE resource using the clusters of cliques.

as described in §2.5, we applied two kinds of clusters annotations: the manual and the automatic processes.


We assigned each person to alternate between working with the full and the restricted versions of the system; half began without, and the others began with.

every passage received four sets of corrections made collaboratively with the system and four sets of corrections made based solely on the participants' internal language models.


The timing statistics are informative, but they reflect the interactions of many factors (e.g., the difficulty of the source text, the quality of the machine translation, the background and motivation of the user).

in the next few subsections, we examine how these factors correlate with the quality of the participant corrections.


Adjectives follow the idafa noun phrase, regardless of which noun in the chain they modify.

example 7 is ambiguous in that the adjective kbyr (big) can modify any of the preceding three nouns.


This rule is motivated by the order of the adjective with respect to its head noun, as well as the idafa construct (see Examples 6 and 7 in Section 2.2.

of applying this rule, the phrase the blank computer screen becomes the screen computer blank .


These constraints contribute to robustness, but cannot be used with the connection path method, since any connection path (parsing action) suggested by the classier can be incorporated into the incremental parse tree.

in the connection path method there are fewer opportunities to correct the errors made by the classiers.


While raw text is an abundant and easily accessible linguistic resource, high-coverage morphological lexicons are scarce or unavailable in Hindi as in many other languages (Clément et al., 2004).

the development of better algorithms for the extraction of morphological lexicons from raw text corpora is a task of considerable importance.


Relational adjectives are derived from nouns and designate a relation between the entity denoted by the noun they are derived from and the entity denoted by the noun they modify.

in a prefixation such as anticostituzionale, the formal base is a relational adjective (costituzionale), but the semantic base is the noun the adjective is derived from (costituzione).


Obviously, the Italian reference lexicon does not contain such information.

we looked for a simple way to automatically extend the Italian lexicon.


If the first one is relatively difficult to implement, the second one can be easily captured using the typical suffixes of such processes.

we considere that any noun ending with suffixes like ione, aggio,or mento are deverbal.


For pronouns, only anaphoric pronouns are so marked.

the system does not learn to recognize non-anaphoric pronounsa significant problem.


Furthermore, typically by this point there will be, say, twenty NPs to share the probability mass, so each one will only get an increase of 0.05.

on the first iteration only the first two sentences have the power to move the distributions, but they do, and they make NPs in the current sentence very slightly more likely to generate the pronoun than the sentence one back, which in turn is more likely than the ones two back.


This slight imbalance is reflected when EM readjusts the probability distribution at the end of the first iteration.

for the second iteration everyone contributes to subsequent imbalances, because it is no longer the case the all antecedents are equally likely.


However, it is likely that each time an n-gram is used in a query, the need for finding more occurrences of this particular n-gram decreases.

we introduced a decay factor δ(s | Q), by which the initial ρ(•) value, written ρ 0 (q s ), is multiplied: The decay is exponential: is a small value between zero and one (for instance 0.05), and ∀s∈Q 1 is the number of times the n-gram s has occurred in past queries.


Since there were practically no singleton words in the English in-domain data, the user-specific vocabulary consists of words occurring at most five times.

the English user-specific words are more frequent than their Spanish and French equivalents, which shows in larger WER reductions for English in all types of adaptation.


The joint distribution is then the product of these functions (and of a normalizing constant).

each F-Node actually represent a factor in the factorization of the joint distribution.


The joint distribution of e,f and a is then: (1) This model only take into consideration oneto-one alignments.

from now on, we will call this model "monolink".


It follows that m e ij (x = i) = k =j b e i (k) = 1 − b e i (j) if the b e i are kept normalized.

at every step, we only need to compute m e ij (j), not m e ij (x = j).


Although it would be possible to learn parameters for the distribution P pc depending on the characteristics of each P-concepts, we want to keep our model simple.

p pc will have only two different values.


However, due to the substantial growth of the multilingual web 1 , queries can be performed and the required information can be found in less common languages, while the query language frequently does not match the language of available information.

if we are looking for information about some lexical category where terms are given in a relatively uncommon language such as Hebrew, it is likely to find more detailed information and more category instances in a salient language such as English.


Then we use the conjecture that terms of the same concept tend to co-appear more frequently than ones belonging to different concepts 3 .

we select a translation of a term co-appearing most frequently with some translation of a different term of the same concept.


"x * y" wildcard that allows to count only co-appearances where x and y are separated by a single word.

we obtain a set of disambiguated term translations.


However, one problem in using it is that it frequently encodes too specific senses and misses common ones.

bear is translated as family Ursidae missing its common "wild animal" sense.


Meanwhile, because COREF drops all states but one after each object is completed, there is a single hypothesized state at each time t whose descendants will ultimately capture all of COREF's probability mass.

for each time t, COREF will retrospectively classify exactly one state as correct.


We are generally interested in whether COREF's experience with previous subjects can be leveraged to improve its interactions with new subjects.

to evaluate our approach, while making maximal use of our available data set, we performed a hold-one-subject-out cross-validation using our 20 human subjects H = {h 1 , ..., h 20 }.


Our approach also depends on having clear evidence about what an interlocutor has said and whether the system has interpreted it correctly-evidence that is often unavailable with spoken input or information-seeking tasks.

even when spoken language interfaces use probabilistic inference for dialogue management (Williams and Young, 2007), new techniques may be needed to mine their experience for correct interpretations.


But this is not sound, as error detection precision is not 100%.

there are variations which clearly must not change.


synonyms, antonyms) or simple syntactic variation account for the entailment only in a small number of pairs.

one essential issue is to identify more complex expressions which, in appropriate contexts, convey the same (or similar) meaning.


The experiment results show that although coverage is still not satisfying, the precision is promising.

our method has the potential to be successfully integrated in a larger entailment detection framework.


By eliminating a negative term from the scoring function, it follows that sim(σ 0 ) > sim(σ).

an alignment σ satisfying σ(i 0 ) = j 0 cannot be optimal and conversely the optimal alignment σ * can never link two arguments with each other if the sum of their weighted syntactic and semantic similarity scores is below B.


In the context of voice local search, users expect overall system response time to be similar to that of web search.

the relatively long ASR latency leaves no room for a slow parser.


We formulate the parsing problem as associating with each token of the input a label indicating whether that token belongs to one of a business listing (bl), city/state (cs) or neither (null).


For example, in one particular case, the annotated SearchTerm is "book stores", for which the ASR 1-best-based parser returned "books" (due to ASR error) as the SearchTerm, while the WCN-based parser identified "banks" as the SearchTerm.

the returned results from the search engine using the 1-best-based parser were more relevant compared to the results returned by the WCN-based parser.


Given WPO, we want to distinguish between The Washington Post is owned by The Washington Post Company, a diversified education and media company and The Post recently went through its third round of job cuts and reported an 11% decline in print advertising revenues for its first quarter, the former being an example of background information whereas the latter is what we would like to appear in the summary.

the similarity to the query alone is not the decisive parameter in computing sentence relevance.


There, so called "business summaries" provide succinct and financially relevant information about the company.


Key differences in these corpora, besides the form of their annotations, include: • SSR aims to correct speech output, while SWBD edit annotation aims to identify reparandum structures specifically.

as mentioned, SSR only marks those reparanda which annotators believe must be deleted to generate a grammatical and contentpreserving reconstruction.


Even in Modern Chinese written text, Classical Chinese elements are not rare.

even if a morpheme represented by a character is no longer used independently in Modern colloquial Chinese, it might still appear to be a free morpheme in modern written text, because it contains Classical Chinese elements.


One way to improve the segmentation and tagging accuracy is to allow non-deterministic segmentation and tagging for Chinese for the reasons stated in Section 1.

our goal is to find a way to transform PKU's tokenizertagger into a system that produces a proper amount of non-deterministic segmentation and tagging results, one that can significantly improve the system's accuracy without a substantial sacrifice in terms of efficiency.


The meeting setting includes a rectangular table with two participants seated at each of its opposite longer sides.

for a given youutterance, we label listeners as either L 1 , L 2 or L 3 depending on whether they are sitting opposite, diagonally or laterally from the speaker.


w n : We say that T is projective if w i is an interval for every word w i .

every node in the dependency structure must dominate a contiguous substring in the sentence.


The Combine steps perform a union operation between subtrees.

the result is a dependency tree containing all the dependent subtrees, and with all of them linked to h: this is the subtree induced by w h .


As WG k is a parser for well-nested structures of gap degree up to k, its Combine steps correspond to all the ways in which we can join two sets of sibling subtrees meeting these constraints, and having a common head, into another.

this parser does not use Combine steps that produce interleaved subtrees, since these would generate items corresponding to ill-nested structures.


As a particular example, the Combine Interleaving Gap C step in Figure 1C is obtained from the string abgab.

we define the parsing schema for MG k , a parser for mildly ill-nested structures for gap degree k, as the schema where (1) the item set is like that of WG k , except that items can now contain any mildly ill-nested structures for gap degree k, instead of being restricted to well-nested structures; and (2) the set of deduction steps consists of a Link step as the one in WG k , plus a set of Combine steps obtained as expressed in Figure 1D.


None of these treebanks contain structures that are strongly ill-nested for their gap degree.

in any of these treebanks, the MG k parser can parse every sentence with gap degree at most k. We have defined a parsing algorithm for wellnested dependency structures with bounded gap degree.


A standard model for extracting biographic facts is to learn templatic contextual patterns such as <NAME> "was born in" <Birthplace>.

such templatic patterns can be learned using seed examples of the attribute in question and, there has been a plethora of work in the seed-based bootstrapping literature which addresses this problem (Ravichandran and Hovy, 2002;Thelen and Riloff, 2002;Mann and Yarowsky, 2005; for our baseline we implemented a standard Ravichandran and Hovy (2002) pattern learning model using 100 seed 2 examples from an online biographic database called NNDB (http://www.nndb.com) for each of the biographic attributes: "Birthdate", "Birthplace", "Deathdate", "Gender", "Nationality", "Occupation" and "Religion".


For example, the distribution of words occuring in a biography Figure 4: Illustration of modeling "occupation" and "nationality" transitively via consensus from attributes of neighboring names of a politician would be different from that of a scientist.

even if the occupation is not explicitly mentioned in the article, one can infer it using a bag-of-words topic profile learned from the seed examples.


In addition, as in other Ethio-Semitic languages, certain roots include inherent vowels and/or gemination (lengthening) of particular consonants.

among the three-consonant roots, there are three subclasses: CCC, CaCC, CC C. As we have seen, the stem of a Semitic verb can be viewed as the result of the insertion of pattern vowels between root consonants and the copying of root consonants in particular positions.